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A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services
This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identit...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828784/ https://www.ncbi.nlm.nih.gov/pubmed/33466730 http://dx.doi.org/10.3390/s21020552 |
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author | Farid, Farnaz Elkhodr, Mahmoud Sabrina, Fariza Ahamed, Farhad Gide, Ergun |
author_facet | Farid, Farnaz Elkhodr, Mahmoud Sabrina, Fariza Ahamed, Farhad Gide, Ergun |
author_sort | Farid, Farnaz |
collection | PubMed |
description | This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication. The framework uses a fusion of electrocardiogram (ECG) and photoplethysmogram (PPG) signals when performing authentication. In addition to relying on the unique identification characteristics of the users’ biometric traits, the security of the framework is empowered by the use of Homomorphic Encryption (HE). The use of HE allows patients’ data to stay encrypted when being processed or analyzed in the cloud. Thus, providing not only a fast and reliable authentication mechanism, but also closing the door to many traditional security attacks. The framework’s performance was evaluated and validated using a machine learning (ML) model that tested the framework using a dataset of 25 users in seating positions. Compared to using just ECG or PPG signals, the results of using the proposed fused-based biometric framework showed that it was successful in identifying and authenticating all 25 users with 100% accuracy. Hence, offering some significant improvements to the overall security and privacy of personalized healthcare systems. |
format | Online Article Text |
id | pubmed-7828784 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78287842021-01-25 A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services Farid, Farnaz Elkhodr, Mahmoud Sabrina, Fariza Ahamed, Farhad Gide, Ergun Sensors (Basel) Article This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication. The framework uses a fusion of electrocardiogram (ECG) and photoplethysmogram (PPG) signals when performing authentication. In addition to relying on the unique identification characteristics of the users’ biometric traits, the security of the framework is empowered by the use of Homomorphic Encryption (HE). The use of HE allows patients’ data to stay encrypted when being processed or analyzed in the cloud. Thus, providing not only a fast and reliable authentication mechanism, but also closing the door to many traditional security attacks. The framework’s performance was evaluated and validated using a machine learning (ML) model that tested the framework using a dataset of 25 users in seating positions. Compared to using just ECG or PPG signals, the results of using the proposed fused-based biometric framework showed that it was successful in identifying and authenticating all 25 users with 100% accuracy. Hence, offering some significant improvements to the overall security and privacy of personalized healthcare systems. MDPI 2021-01-14 /pmc/articles/PMC7828784/ /pubmed/33466730 http://dx.doi.org/10.3390/s21020552 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Farid, Farnaz Elkhodr, Mahmoud Sabrina, Fariza Ahamed, Farhad Gide, Ergun A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services |
title | A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services |
title_full | A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services |
title_fullStr | A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services |
title_full_unstemmed | A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services |
title_short | A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services |
title_sort | smart biometric identity management framework for personalised iot and cloud computing-based healthcare services |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828784/ https://www.ncbi.nlm.nih.gov/pubmed/33466730 http://dx.doi.org/10.3390/s21020552 |
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